Hadoop Big Data Analytics

The company opted for a solution that involved migrating their analytics platform from Oracle to Hadoop using an automated data and metadata migration tool. The key features of the solution  were: 

• Automatic discovery and migration of Oracle database schemas to Hadoop. 
• Large-scale data migration with parallel processing to minimize downtime and ensure minimal disruption to business operations. 
• Automatic conversion of SQL queries to Hive queries for seamless migration of existing analytics applications. 
• Built-in data quality checks to ensure data accuracy and completeness. 
• Automated metadata migration to ensure continuity of data lineage and governance. 

The migration was done in a phased manner, with a series of tests to validate the data and ensure that the new Hadoop-based platform was able to meet the company’s analytics needs. 

The migration to a Hadoop-based analytics platform had several benefits for the Fintech company, including: 

• Improved scalability and performance, enabling the company to handle larger volumes of data and process analytics in a timely manner. 

• Significant cost savings due to the open-source nature of the Hadoop platform and the ability to run on commodity hardware. 
• Greater agility and flexibility in managing data and processing analytics, with the ability to handle unstructured and semi-structured data. 
• Better support for data governance and lineage due to the automation of metadata migration. 
• The ability to leverage the Hadoop ecosystem and its rich set of tools and technologies for advanced analytics and data processing. 

The migration to a Hadoop-based analytics platform proved to be a game-changer for the Fintech company, enabling them to take their analytics capabilities to the next level and stay competitive in their industry.